Agent Systems
While reasoning systems excel at logical analysis and problem-solving, agent systems take automation to a new level by adding autonomous action and interaction capabilities. Agents can not only reason about problems but also actively execute solutions by managing their own planning, using tools, maintaining state, and even collaborating with other agents. This enables a shift from passive analysis to active task completion - agents can break down complex goals, select appropriate tools, monitor their own progress, and adapt their strategies based on results.
Planning and Decision-making
Goal Decomposition
- Task Hierarchies
- Subtask Planning
- Priority Management
- Resource Allocation
Action Selection
- Policy Learning
- Decision Trees
- Utility Functions
- Risk Assessment
Execution Monitoring
- Progress Tracking
- Error Detection
- Recovery Strategies
- Performance Optimization
Tool Use and API Integration
Tool Selection
- Capability Matching
- Context Awareness
- Tool Composition
- Fallback Handling
API Orchestration
- Request Management
- Error Handling
- Rate Limiting
- Response Processing
Function Calling
- Parameter Validation
- Type Checking
- Return Value Handling
- Error Propagation
Memory and State Management
Short-term Memory
- Working Memory Buffer
- Attention Mechanisms
- Priority Queuing
- Garbage Collection
Long-term Memory
- Knowledge Base Integration
- Experience Replay
- Memory Consolidation
- Forgetting Mechanisms
State Tracking
- Context Windows
- History Management
- Checkpoint Systems
- Recovery Mechanisms